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Predicting Catastrophic Events Using Machine Learning Models for Natural Language Processing

Predicting Catastrophic Events Using Machine Learning Models for Natural Language Processing
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Author(s): Muskaan Chopra (Chandigarh College of Engineering and Technology, India), Sunil K. Singh (Chandigarh College of Engineering and Technology, India), Kriti Aggarwal (Chandigarh College of Engineering and Technology, India) and Anshul Gupta (Chandigarh College of Engineering and Technology, India)
Copyright: 2022
Pages: 21
Source title: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India), Dragan Peraković (University of Zagreb, Croatia), Ahmed A. Abd El-Latif (Menoufia University, Egypt) and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-8413-2.ch010

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Abstract

In recent years, there has been widespread improvement in communication technologies. Social media applications like Twitter have made it much easier for people to send and receive information. A direct application of this can be seen in the cases of disaster prediction and crisis. With people being able to share their observations, they can help spread the message of caution. However, the identification of warnings and analyzing the seriousness of text is not an easy task. Natural language processing (NLP) is one way that can be used to analyze various tweets for the same. Over the years, various NLP models have been developed that are capable of providing high accuracy when it comes to data prediction. In the chapter, the authors will analyze various NLP models like logistic regression, naive bayes, XGBoost, LSTM, and word embedding technologies like GloVe and transformer encoder like BERT for the purpose of predicting disaster warnings from the scrapped tweets. The authors focus on finding the best disaster prediction model that can help in warning people and the government.

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